Conversations across the medical communications industry make one thing clear, artificial intelligence is no longer a side topic. It is rapidly becoming central to how the industry works. At the same time, there is a healthy and necessary focus on ethics, governance, transparency, and quality. The momentum is real, the opportunities are growing, and the responsibility is non-negotiable.
AI has moved from experimentation and pilots into expectation. Clients increasingly want to know not only whether AI is used, but also how it is controlled, how outputs are verified, how privacy is protected, and how transparency and disclosure are handled. The organizations that lead will be those that combine momentum with rigor and innovation with trust.
Building the AI business case: understanding the operational impact
One of the most valuable exercises I have seen teams work through is putting together a credible business case for AI. The best versions are hands-on and built around a simple reality: the hard part is not generating text, but making sure workflows are safe, high-quality, and scalable.
The discipline this requires is the most useful part. You have to define the problem clearly, identify value, map the workflow end-to-end, surface risks, and design human quality controls from the start rather than bolting them on later.
Some of the most promising use cases are already reshaping how core publications work is done. AI is enabling editorial checks on congress data, turning strategic publications planning into a process that is continuously updated rather than a static PowerPoint deck, and supporting medical strategy through internal agents that develop messaging options, surface relevant data, including HEOR considerations, and assist with key opinion leader ranking.
Steering committee operations are also evolving, with AI supporting investigator profiling, evidence-based selection, and meeting preparation.
The biggest takeaway: when you examine ideas through a risk and scale lens, you quickly see which concepts are interesting demonstrations and which are viable candidates for real operational impact.
Quality versus innovation, and a more human direction
There is tension of sorts running through the industry. Should the focus be on quality first, or push innovation harder? The most compelling view is that we must do both, in the right order for each use case, and always with scientific integrity as the foundation.
Eric Topol’s 2019 book Deep Medicine offers a compelling parallel that if AI can make medicine more human, medical communications can follow the same path. The idea is not to replace expertise, but to return time and attention to the work only humans do well – judgment, context, empathy, and strategic interpretation.
Two sentiments come up repeatedly in conversations with clients and peers, often in different words but with the same meaning:
“We are wary of AI in medical communications because of hallucinations, bias, and accuracy concerns.”
“Inertia is the paralysis that can stall innovation. Collaboration is the key to breaking that barrier.”
That rings true for all of us. The industry is moving, but it is moving carefully. The teams that succeed will be the ones that build trust through governance, transparency, and repeatable quality.
Healthcare is an engine of acceleration for AI
Healthcare is one of the strongest drivers of AI evolution. The demand for medical information will continue to grow, and more healthcare-specific tools are emerging for providers, patients, and developers. This is shaping expectations as people want faster access to clearer information while still demanding clinical accuracy and responsibility. Around 5% of all ChatGPT queries are health-related – that’s not something you would want to get wrong!
The shift ahead is modular, reasoning AI
There is a clear shift away from bespoke builds toward modular, reusable capabilities. The direction of travel is to create components that can be reused across workflows, then composed into agents and processes with consistent governance. That makes scaling more realistic and reduces the risk of a fragmented tool landscape. This is the approach that we are already pursuing at Envision AI Labs.
The progression appears to follow three stages: large language model establishment between 2021 and 2024; AI agents from 2025 onward that retrieve and act on grounded information to reduce hallucinations; and now a shift toward more advanced reasoning approaches that can handle structured workflows and provide more strategic support. While the technology is maturing, the operating model and controls determine whether it creates real value. Each step needs to be thought out properly first, otherwise errors compound quickly.
What good practice looks like
Generative AI is being used as a productivity accelerator across medical writing workflows, commonly applied to screening, extraction, formatting, translation, ideation, and analysis. But these benefits only hold when paired with strong human oversight, quality assurance by design, and governance. The risks around accuracy, hallucinations, scientific integrity, and data privacy remain real.
Human-in-the-loop AI is non-negotiable. The hybrid approach, where AI does a first pass and humans verify and refine, often improves accuracy and reinforces something important: writers and editors remain essential for scientific fidelity, judgment calls, and strategic interpretation. References and citations deserve particular attention. Fabricated or misattributed references can quickly erase any speed gains if verification is not planned from the start.
Plain language summaries are becoming standard, but truly breakthrough outcomes remain elusive. The gap between publication and availability of a plain language summary can stretch beyond 12 months, and engagement remains low. AI-generated summaries that improve metadata and search optimization could increase discovery of the original scientific content, but the focus should be on measurable outcomes rather than automation for its own sake.
What this means in reality
Three priorities should guide any organization navigating AI in medical communications. First, prioritize use cases that are high value and high control, grounded in approved sources with robust human review. Second, treat AI literacy as a competitive capability: skills gaps, governance uncertainty, and privacy concerns remain real blockers, and the organizations building practical literacy and safe experimentation pathways are already moving faster. Third, keep principles explicit and the human review built in with grounded and traceable sources, leading privacy and security, and clear accountability.
AI is transforming medical communications quickly. The goal is not to automate expertise but to amplify it. Innovation paired with integrity alongside speed paired with transparency; that is how the industry moves forward responsibly and delivers better outcomes for clients and patients.
The case for combined intelligence
Underlying all of this is a principle we hold firmly at Envision. The most powerful combination in this space is human expertise working alongside advanced AI. We call this combined intelligence. Scientific knowledge, strategic judgment, and clinical nuance remain human strengths. AI contributes speed, scale, and the ability to surface information in ways that would otherwise be virtually impossible. Together, it ensures that the high-quality approaches we develop today close the gap between scientific innovation and the patients who need it most.
If you are exploring where AI could fit in your workflows, would value a space to explore and refine innovation ideas, or want to pilot an approach, we would welcome the conversation.
